ScholarSphere Newsletter #X

Where AI meets Academia

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Scholar Sphere
June 9th, 2024

Welcome to “X” edition of ScholarSphere

“A stitch in time saves nine ”

Welcome to our AI Newsletter—your ultimate guide to the rapidly changing world of AI in academia. If you haven't joined us yet, now's your chance! Click that button, subscribe with your email, and get ready for an exciting journey through all things AI in the academic realm!

Deep Dive into AI: Expand Your Knowledge

A Gentle Introduction to Markov Chain Monte Carlo for Probability
By Jason Brownlee, machinelearningmastery

Text Generation with Markov Chains: An Introduction to using Markovify By Gregory Pernicano

Challenge of Probabilistic Inference 
Calculating a quantity from a probabilistic model is referred to more generally as probabilistic inference, or simply inference. For example, we may be interested in calculating an expected probability, estimating the density, or other properties of the probability distribution. This is the goal of the probabilistic model, and the name of the inference performed often takes on the name of the probabilistic model, e.g. Bayesian Inference is performed with a Bayesian probabilistic model.

What Is Markov Chain Monte Carlo
The solution to sampling probability distributions in high-dimensions is to use Markov Chain Monte Carlo, or MCMC for short. The most popular method for sampling from high-dimensional distributions is Markov chain Monte Carlo or MCMC.

Monte Carlo 
Monte Carlo is a technique for randomly sampling a probability distribution and approximating a desired quantity. Monte Carlo algorithms, [….] are used in many branches of science to estimate quantities that are difficult to calculate exactly.

Markov Chain
Markov chain is a systematic method for generating a sequence of random variables where the current value is probabilistically dependent on the value of the prior variable. Specifically, selecting the next variable is only dependent upon the last variable in the chain. A Markov chain is a special type of stochastic process, which deals with characterization of sequences of random variables. Special interest is paid to the dynamic and the limiting behaviors of the sequence.

Markov Chain Monte Carlo 
Combining these two methods, Markov Chain and Monte Carlo, allows random sampling of high-dimensional probability distributions that honors the probabilistic dependence between samples by constructing a Markov Chain that comprise the Monte Carlo sample. MCMC is essentially Monte Carlo integration using Markov chains. […] Monte Carlo integration draws samples from the the required distribution, and then forms sample averages to approximate expectations. Markov chain Monte Carlo draws these samples by running a cleverly constructed Markov chain for a long time.

Markov Chain Monte Carlo

Markov Chain Monte Carlo Algorithms 
There are many Markov Chain Monte Carlo algorithms that mostly define different ways of constructing the Markov Chain when performing each Monte Carlo sample.

Gibbs Sampling Algorithm
The Gibbs Sampling algorithm is an approach to constructing a Markov chain where the probability of the next sample is calculated as the conditional probability given the prior sample. Samples are constructed by changing one random variable at a time, meaning that subsequent samples are very close in the search space, e.g. local. As such, there is some risk of the chain getting stuck. The idea behind Gibbs sampling is that we sample each variable in turn, conditioned on the values of all the other variables in the distribution.

Metropolis-Hastings Algorithm 
The Metropolis-Hastings Algorithm is appropriate for those probabilistic models where we cannot directly sample the so-called next state probability distribution, such as the conditional probability distribution used by Gibbs Sampling. Unlike the Gibbs chain, the algorithm does not assume that we can generate next-state samples from a particular target distribution.

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Mastering AI: Prompt Perfection

How to Write ChatGPT Prompts for Email

It’s important to include 5 steps in your ChatGPT prompts in order to get the best results. Those would be:

1 Context.
For example: You are an experienced content writer with high levels of expertise and authority within the tech industry.

2 Task.
For example: Your task is to write content that will be published online on websites, social media, email newsletters, and in advertisements. Your writing style is informative, friendly, engaging, while incorporating humor and real-life examples.

3 Instruct.
For example: I will provide you with a topic or series of topics and you will come up with an engaging article outline for this topic.

4 Clarify.
For example: Do you understand?

5 Refine. 
Rewrite using more natural, expressive language and include some examples to accompany this information.

Full Example: Job application email
You are an experienced account executive in B2B enterprise software sales with a strong track record of successful deal closures and excellent communication skills.
Your task is to write a professional cover letter email tailored to a specific company and role. The email should highlight your skills and experience, emphasizing your fit for the account executive position. Ensure the email is correctly formatted, concise, clear, and neutral in tone.
I will provide you with information about your skills and experience. Your task is to write a professional cover letter email tailored to a CRM solutions company, showcasing why you are a great fit for the account executive role.
Do you understand?

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Cutting-Edge AI Insights for Academia

OpenAI is making ChatGPT cheaper for schools and nonprofits
Google Unveils LearnLM: AI Models Tailored for Enhanced Learning Experiences
ChatGPT Edu now available in select universities
Reimagining Civic Learning in Future Cities

Article of the Week: The opportunities and challenges of ChatGPT in education by Ibrahim Adeshola

Spotlight on AI Tools for Academic Excellence

Edugpt: Managed AI for the Classroom For Students, A Study-buddy for your Home Personalized Learning. For Parents, Homeschooling Redefined with AI A New World of Educational Possibilities
Segmind: Provides serverless APIs for hundreds of generative models that can be applied to a specific task that your application wants to accomplish. You can grab the APIs from the model page to get started with integrating them with your app.
Smodin: An AI-powered writing assistant that provides various tools to help with all aspects of writing and research. It offers plagiarism checking, citations, grammar corrections, translations, text generation, and more.
Powerdrill: Built for bridging your data and AI. It provides the services and platform for no-code and one-stop integration of your data and OpenAI large language models (LLMs) for intelligent Q&As and ecosystem interaction. Powerdrill is currently free for trial.
Writesonic: An AI-powered writing tool used by content writers to increase productivity, overcome writer's block, and improve writing skills. It generates factually-accurate, on-brand content with real-time data, optimizes for SEO, and allows users to customize their AI chatbots.

Academic Frontiers: Exploring AI Innovations

The six use cases of AI in classrooms that will change education in 2024 
By Universitat Oberta de Catalunya

(source: eLinC, UOC)

1. Avatars in multiple languages
One of the most dramatic changes will be the widespread use of AI to create videos of avatars, featuring real or fictional people, which use natural language in various tongues. Using production tools such as HeyGen or Synthesia, these computer-generated representations of human beings will help provide a more personalized and accessible educational experience on a global scale. For example, they will enable teachers to create their own avatars that teach in their students' native language, even if they do not really speak it themselves.

2. Course preparation
Another new factor to take into account will be how teaching staff use this generative technology to prepare their courses at various stages in the educational process. Conversational tools, such as ChatGPT, or tools, like ChatPDF, will help them to plan their course, search for and index information, outline methodological proposals, and suggest online educational resources, among other things.

3. Services for education through APIs
There is also a growing interest in pushing the use of AI in teaching thanks to specialist start-ups developing APIs for use in education. These application programming interfaces (APIs) for artificial intelligence tools provide innovative and specific services for education. This opens up the possibility of adding some degree of automation to tasks including content creation, student assessment or class management. New applications are also being developed to allow any user to create personalized AI educational tools, thereby enhancing the democratization of assisted learning.

4. Integration in art courses
The fourth trend is related to the inevitable integration of AI in art courses as a creative tool for producing innovative and previously impossible works, or assisting in the creative process. Midjourney, DALL·E and Runway are some of the platforms that already allow users to create images and video in order to produce highly complex works of visual art, and they are widely used by an ever-increasing number of professionals specializing in these artistic fields.

5. Personalized learning experiences
Adaptive learning based on AI and the student's actions will reach the education sector in the form of a combination of advanced technologies and pedagogical methodologies based on data exploitation. The objective is to optimize education based on each student's unique needs and progress, enabling materials and learning experiences to be created or adapted in a personalized way.

6. Infographics, presentations, and glossaries: time and cost savings
 Finally, the last trend is related to creating learning resources in different ways, and reducing the work involved in producing them. These possibilities include support when structuring, devising and creating infographics, creating slideshows with tools such as SlidesAI or Tome, and contributing to the creation of glossaries. This will all help teachers reduce outsourcing and costs, and the time spent on producing content.

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Demystifying AI: An Exploration of Explainable Artificial Intelligence (XAI)
Prepared by Sina Bastani

Artificial Intelligence (AI) has become deeply embedded in various sectors like healthcare, finance, and automated transport. However, the complexity and opacity of many AI models, often called "black-box" models, pose significant challenges in understanding and interpreting their decision-making processes. This lack of transparency has led to the emergence of Explainable AI (XAI), which aims to make AI more interpretable and trustworthy. Vikas Hassija and his colleagues have provided a comprehensive review of XAI, highlighting the critical need for transparency in AI systems. They explain that the primary goals of XAI include building trust, meeting regulatory requirements, identifying biases, ensuring model generalization, and facilitating debugging.

The review discusses the challenges of interpreting black-box models and the various methods developed to make these models more understandable. The authors categorize interpretability into perceptive and mathematical interpretability. Perceptive interpretability involves visual aids that make models more understandable to humans, while mathematical interpretability explore the mechanisms behind neural network layers. They also address the inherent challenges in XAI, such as the complexity of models like Deep Neural Networks (DNNs), and propose solutions like model distillation and rule extraction.

The review highlights the importance of XAI in various application domains, including healthcare, finance, and automated transport, where the need for transparency is critical. For instance, in healthcare, XAI can help medical professionals understand AI's recommendations, thereby improving patient safety and trust. In finance, XAI ensures compliance with regulatory requirements and helps build trust by explaining decisions made by AI models.

 Looking forward, the authors suggest future research directions for XAI, including the development of Responsible AI, which encompasses ethical considerations in AI development. They also call for the creation of a universal framework to standardize methodologies and evaluation metrics for XAI. Additionally, integrating XAI techniques with Generative Pretrained Transformers (GPT) models can provide explanations for their predictions, making these models more transparent.

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Engage and Learn: AI Workshops & Seminars

And that's a wrap for this edition of our AI Newsletter! We've covered a lot of ground, but remember, the adventure doesn't stop here. Keep exploring, keep learning, and keep pushing the boundaries of what's possible with AI. Together, we're shaping the future of academia one breakthrough at a time. Until next time, stay curious and keep shining bright! ✨. If you want to contact editorial team for having your news, tool, website, X page, or even yourself! introduced in our newsletter you can find contact info just below.

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